Process Data

Extract data for 3 sites from each parquet file.

Reformat to wide dataframe.

Resample to X-minute.

Add features.

Min/Max scaler.

Save to /processed/

Imports and Setup

from pathlib import Path
import pandas as pd
import numpy as np
import joblib
from sklearn.preprocessing import OneHotEncoder, MinMaxScaler
import hvplot.pandas

pqdir = Path('c:/projects/nrel-presentation/data/interim')

Extract data for sites

upstream site = 447_2_351

main site = 446_1_350

downstream site = 446_3_349

sites = ['447_2_351', '446_1_350', '446_3_349']

all_results = []
all_pq = pqdir.glob('*.pq')
i = 0
for this_pq in all_pq:
    print(i, end=', ')
    df = pd.read_parquet(this_pq)

    # site: 446_1_350
    a = df[(df['detector_id'] == sites[1]) & (df['detector_lane_number'] == 1)][['timestamp', 'lane_vehicle_speed', 'lane_vehicle_count', 'occupancy']]
    
    if not a.empty:
        a.index = [0]

        # upstream site: 447_2_351
        us = df[(df['detector_id'] == sites[0]) & (df['detector_lane_number'] == 1)][['lane_vehicle_speed', 'lane_vehicle_count', 'occupancy']]
        if us.empty:
            us = pd.DataFrame([[np.nan, np.nan, np.nan]], columns=['us_lane_vehicle_speed', 'us_lane_vehicle_count', 'us_occupancy'])
            print('us empty')
        else:
            us.index = [0]

        # downstream site: 446_3_349
        ds = df[(df['detector_id'] == sites[2]) & (df['detector_lane_number'] == 1)][['lane_vehicle_speed', 'lane_vehicle_count', 'occupancy']]
        if ds.empty:
            ds = pd.DataFrame([[np.nan, np.nan, np.nan]], columns=['ds_lane_vehicle_speed', 'ds_lane_vehicle_count', 'ds_occupancy'])
            print('ds empty')
        else:
            ds.index = [0]


    D = pd.concat([a, us, ds], axis=1, ignore_index=True)

    all_results.append(D)
    
    i += 1
    
D2 = pd.concat(all_results)
D2.columns = ['timestamp', 'lane_vehicle_speed', 'lane_vehicle_count', 'occupancy', \
              'ds_lane_vehicle_speed', 'ds_lane_vehicle_count', 'ds_occupancy', 'us_lane_vehicle_speed', 'us_lane_vehicle_count', 'us_occupancy']

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19279, 19280, 19281, 19282, 19283, 19284, 19285, 19286, 19287, 19288, 19289, 19290, 19291, 19292, 19293, 19294, 19295, 19296, 19297, 19298, 19299, 19300, 19301, 19302, 19303, 19304, 19305, 19306, 19307, 19308, 19309, 19310, 19311, 19312, 19313, 19314, 19315, 19316, 19317, 19318, 19319, 19320, 19321, 19322, 19323, 19324, 19325, 19326, 19327, 19328, 19329, 19330, 19331, 19332, 19333, 19334, 19335, 19336, 19337, 19338, 19339, 19340, 19341, 19342, 19343, 19344, 19345, 19346, 19347, 19348, 19349, 19350, 19351, 19352, 19353, 19354, 19355, 19356, 19357, 19358, 19359, 19360, 19361, 19362, 19363, 19364, 19365, 19366, 19367, 19368, 19369, 19370, 19371, 19372, 19373, 19374, 19375, 19376, 19377, 19378, 19379, 19380, 19381, 19382, 19383, 19384, 19385, 19386, 19387, 19388, 19389, 19390, 19391, 19392, 19393, 19394, 19395, 19396, 19397, 19398, 19399, 19400, 19401, 19402, 19403, 19404, 19405, 19406, 19407, 19408, 19409, 19410, 19411, 19412, 19413, 19414, 19415, 19416, 19417, 19418, 19419, 19420, 19421, 19422, 19423, 19424, 19425, 19426, 19427, 19428, 19429, 19430, 19431, 19432, 19433, 19434, 19435, 19436, 19437, 19438, 19439, 19440, 19441, 19442, 19443, 19444, 19445, 19446, 19447, 19448, 19449, 19450, 19451, 19452, 19453, 19454, 19455, 19456, 19457, 19458, 19459, 19460, 19461, 19462, 19463, 19464, 19465, 19466, 19467, 19468, 19469, 19470, 19471, 19472, 19473, 19474, 19475, 19476, 19477, 19478, 19479, 19480, 19481, 19482, 19483, 19484, 19485, 19486, 19487, 19488, 19489, 19490, 19491, 19492, 19493, 19494, 19495, 19496, 19497, 19498, 19499, 19500, 19501, 19502, 19503, 19504, 19505, 19506, 19507, 19508, 19509, 19510, 19511, 19512, 19513, 19514, 19515, 19516, 19517, 19518, 19519, 19520, 19521, 19522, 19523, 19524, 19525, 19526, 19527, 19528, 19529, 19530, 19531, 19532, 19533, 19534, 19535, 19536, 19537, 19538, 19539, 19540, 19541, 19542, 19543, 19544, 19545, 19546, 19547, 19548, 19549, 19550, 19551, 19552, 19553, 19554, 19555, 19556, 19557, 19558, 19559, 19560, 19561, 19562, 19563, 19564, 19565, 19566, 19567, 19568, 19569, 19570, 19571, 19572, 19573, 19574, 19575, 19576, 19577, 19578, 19579, 19580, 19581, 19582, 19583, 19584, 19585, 19586, 19587, 19588, 19589, 19590, 19591, 19592, 19593, 19594, 19595, 19596, 19597, 19598, 19599, 19600, 19601, 19602, 19603, 19604, 19605, 19606, 19607, 19608, 19609, 19610, 19611, 19612, 19613, 19614, 19615, 19616, 19617, 19618, 19619, 19620, 19621, 19622, 19623, 19624, 19625, 19626, 19627, 19628, 19629, 19630, 19631, 19632, 19633, 19634, 19635, 19636, 19637, 19638, 19639, 19640, 19641, 19642, 19643, 19644, 19645, 19646, 19647, 19648, 19649, 19650, 19651, 19652, 19653, 19654, 19655, 19656, 19657, 19658, 19659, 19660, 19661, 19662, 19663, 19664, 19665, 19666, 19667, 19668, 19669, 19670, 19671, 19672, 19673, 19674, 19675, 19676, 19677, 19678, 19679, 19680, 19681, 19682, 19683, 19684, 19685, 19686, 19687, 19688, 19689, 19690, 19691, 19692, 19693, 19694, 19695, 19696, 19697, 19698, 19699, 19700, 19701, 19702, 19703, 19704, 19705, 19706, 19707, 19708, 19709, 19710, 19711, 19712, 19713, 19714, 19715, 19716, 19717, 19718, 19719, 19720, 19721, 19722, 19723, 19724, 19725, 19726, 19727, 19728, 19729, 19730, 19731, 19732, 19733, 19734, 19735, 19736, 19737, 19738, 19739, 19740, 19741, 19742, 19743, 19744, 19745, 19746, 19747, 19748, 19749, 19750, 19751, 19752, 19753, 19754, 19755, 19756, 19757, 19758, 19759, 19760, 19761, 19762, 19763, 19764, 19765, 19766, 19767, 19768, 19769, 19770, 19771, 19772, 19773, 19774, 19775, 19776, 19777, 19778, 19779, 19780, 19781, 19782, 19783, 19784, 19785, 19786, 19787, 19788, 19789, 19790, 19791, 19792, 19793, 19794, 19795, 19796, 19797, 19798, 19799, 19800, 19801, 19802, 19803, 19804, 19805, 19806, 19807, 19808, 19809, 19810, 19811, 19812, 19813, 19814, 19815, 19816, 19817, 19818, 19819, 19820, 19821, 19822, 19823, 19824, 19825, 19826, 19827, 19828, 19829, 19830, 19831, 19832, 19833, 19834, 19835, 19836, 19837, 19838, 19839, 19840, 19841, 19842, 19843, 19844, 19845, 19846, 19847, 19848, 19849, 19850, 19851, 19852, 19853, 19854, 19855, 19856, 19857, 19858, 19859, 19860, 19861, 19862, 19863, 19864, 19865, 19866, 19867, 19868, 19869, 19870, 19871, 19872, 19873, 19874, 19875, 19876, 19877, 19878, 19879, 19880, 19881, 19882, 19883, 19884, 19885, 19886, 19887, 19888, 19889, 19890, 19891, 19892, 19893, 19894, 19895, 19896, 19897, 19898, 19899, 19900, 19901, 19902, 19903, 19904, 19905, 19906, 19907, 19908, 19909, 19910, 19911, 19912, 19913, 19914, 19915, 19916, 19917, 19918, 19919, 19920, 19921, 19922, 19923, 19924, 19925, 19926, 19927, 19928, 19929, 19930, 19931, 19932, 19933, 19934, 19935, 19936, 19937, 19938, 19939, 19940, 19941, 19942, 19943, 19944, 19945, 19946, 19947, 19948, 19949, 19950, 19951, 19952, 19953, 19954, 19955, 19956, 19957, 19958, 19959, 19960, 19961, 19962, 19963, 19964, 19965, 19966, 19967, 19968, 19969, 19970, 19971, 19972, 19973, 19974, 19975, 19976, 19977, 19978, 19979, 19980, 19981, 19982, 19983, 19984, 19985, 19986, 19987, 19988, 19989, 19990, 19991, 19992, 19993, 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20709, 20710, 20711, 20712, 20713, 20714, 20715, 20716, 20717, 20718, 20719, 20720, 20721, 20722, 20723, 20724, 20725, 20726, 20727, 20728, 20729, 20730, 20731, 20732, 20733, 20734, 20735, 20736, 20737, 20738, 20739, 20740, 20741, 20742, 20743, 20744, 20745, 20746, 20747, 20748, 20749, 20750, 20751, 20752, 20753, 20754, 20755, 20756, 20757, 20758, 20759, 20760, 20761, 20762, 20763, 20764, 20765, 20766, 20767, 20768, 20769, 20770, 20771, 20772, 20773, 20774, 20775, 20776, 20777, 20778, 20779, 20780, 20781, 20782, 20783, 20784, 20785, 20786, 20787, 20788, 20789, 20790, 20791, 20792, 20793, 20794, 20795, 20796, 20797, 20798, 20799, 20800, 20801, 20802, 20803, 20804, 20805, 20806, 20807, 20808, 20809, 20810, 20811, 20812, 20813, 20814, 20815, 20816, 20817, 20818, 20819, 20820, 20821, 20822, 20823, 20824, 20825, 20826, 20827, 20828, 20829, 20830, 20831, 20832, 20833, 20834, 20835, 20836, 20837, 20838, 20839, 20840, 20841, 20842, 20843, 20844, 20845, 20846, 20847, 20848, 20849, 20850, 20851, 20852, 20853, 20854, 20855, 20856, 20857, 20858, 20859, 20860, 20861, 20862, 20863, 20864, 20865, 20866, 20867, 20868, 20869, 20870, 20871, 20872, 20873, 20874, 20875, 20876, 20877, 20878, 20879, 20880, 20881, 20882, 20883, 20884, 20885, 20886, 20887, 20888, 20889, 20890, 20891, 20892, 20893, 20894, 20895, 20896, 20897, 20898, 20899, 20900, 20901, 20902, 20903, 20904, 20905, 20906, 20907, 20908, 20909, 20910, 20911, 20912, 20913, 20914, 20915, 20916, 20917, 20918, 20919, 20920, 20921, 20922, 20923, 20924, 20925, 20926, 20927, 20928, 20929, 20930, 20931, 20932, 20933, 20934, 20935, 20936, 20937, 20938, 20939, 20940, 20941, 20942, 20943, 20944, 20945, 20946, 20947, 20948, 20949, 20950, 20951, 20952, 20953, 20954, 20955, 20956, 20957, 20958, 20959, 20960, 20961, 20962, 20963, 20964, 20965, 20966, 20967, 20968, 20969, 20970, 20971, 20972, 20973, 20974, 20975, 20976, 20977, 20978, 20979, 20980, 20981, 20982, 20983, 20984, 20985, 20986, 20987, 20988, 20989, 20990, 20991, 20992, 20993, 20994, 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26143, 26144, 26145, 26146, 26147, 26148, 26149, 26150, 26151, 26152, 26153, 26154, 26155, 26156, 26157, 26158, 26159, 26160, 26161, 26162, 26163, 26164, 26165, 26166, 26167, 26168, 26169, 26170, 26171, 26172, 26173, 26174, 26175, 26176, 26177, 26178, 26179, 26180, 26181, 26182, 26183, 26184, 26185, 26186, 26187, 26188, 26189, 26190, 26191, 26192, 26193, 26194, 26195, 26196, 26197, 26198, 26199, 26200, 26201, 26202, 26203, 26204, 26205, 26206, 26207, 26208, 26209, 26210, 26211, 26212, 26213, 26214, 26215, 26216, 26217, 26218, 26219, 26220, 26221, 26222, 26223, 26224, 26225, 26226, 26227, 26228, 26229, 26230, 26231, 26232, 26233, 26234, 26235, 26236, 26237, 26238, 26239, 26240, 26241, 26242, 26243, 26244, 26245, 26246, 26247, 26248, 26249, 26250, 26251, 26252, 26253, 26254, 26255, 26256, 26257, 26258, 26259, 26260, 26261, 26262, 26263, 26264, 26265, 26266, 26267, 26268, 26269, 26270, 26271, 26272, 26273, 26274, 26275, 26276, 26277, 26278, 26279, 26280, 26281, 26282, 26283, 26284, 26285, 26286, 26287, 26288, 26289, 26290, 26291, 26292, 26293, 26294, 26295, 26296, 26297, 26298, 26299, 26300, 26301, 26302, 26303, 26304, 26305, 26306, 26307, 26308, 26309, 26310, 26311, 26312, 26313, 26314, 26315, 26316, 26317, 26318, 26319, 26320, 26321, 26322, 26323, 26324, 26325, 26326, 26327, 26328, 26329, 26330, 26331, 26332, 26333, 26334, 26335, 26336, 26337, 26338, 26339, 26340, 26341, 26342, 26343, 26344, 26345, 26346, 26347, 26348, 26349, 26350, 26351, 26352, 26353, 26354, 26355, 26356, 26357, 26358, 26359, 26360, 26361, 26362, 26363, 26364, 26365, 26366, 26367, 26368, 26369, 26370, 26371, 26372, 26373, 26374, 26375, 26376, 26377, 26378, 26379, 26380, 26381, 26382, 26383, 26384, 26385, 26386, 26387, 26388, 26389, 26390, 26391, 26392, 26393, 26394, 26395, 26396, 26397, 26398, 26399, 26400, 26401, 26402, 26403, 26404, 26405, 26406, 26407, 26408, 26409, 26410, 26411, 26412, 26413, 26414, 26415, 26416, 26417, 26418, 26419, 26420, 26421, 26422, 26423, 26424, 26425, 26426, 26427, 26428, 26429, 26430, 26431, 26432, 26433, 26434, 26435, 26436, 26437, 26438, 26439, 26440, 26441, 26442, 26443, 26444, 26445, 26446, 26447, 26448, 26449, 26450, 26451, 26452, 26453, 26454, 26455, 26456, 26457, 26458, 26459, 26460, 26461, 26462, 26463, 26464, 26465, 26466, 26467, 26468, 26469, 26470, 26471, 26472, 26473, 26474, 26475, 26476, 26477, 26478, 26479, 26480, 26481, 26482, 26483, 26484, 26485, 26486, 26487, 26488, 26489, 26490, 26491, 26492, 26493, 26494, 26495, 26496, 26497, 26498, 26499, 26500, 26501, 26502, 26503, 26504, 26505, 26506, 26507, 26508, 26509, 26510, 26511, 26512, 26513, 26514, 26515, 26516, 26517, 26518, 26519, 26520, 26521, 26522, 26523, 26524, 26525, 26526, 26527, 26528, 26529, 26530, 26531, 26532, 26533, 26534, 26535, 26536, 26537, 26538, 26539, 26540, 26541, 26542, 26543, 26544, 26545, 26546, 26547, 26548, 26549, 26550, 26551, 26552, 26553, 26554, 26555, 26556, 26557, 26558, 26559, 26560, 26561, 26562, 26563, 26564, 26565, 26566, 26567, 26568, 26569, 26570, 26571, 26572, 26573, 26574, 26575, 26576, 26577, 26578, 26579, 26580, 26581, 26582, 26583, 26584, 26585, 26586, 26587, 26588, 26589, 26590, 26591, 26592, 26593, 26594, 26595, 26596, 26597, 26598, 26599, 26600, 26601, 26602, 26603, 26604, 26605, 26606, 26607, 26608, 26609, 26610, 26611, 26612, 26613, 26614, 26615, 26616, 26617, 26618, 26619, 26620, 26621, 26622, 26623, 26624, 26625, 26626, 26627, 26628, 26629, 26630, 26631, 26632, 26633, 26634, 26635, 26636, 26637, 26638, 26639, 26640, 26641, 26642, 26643, 26644, 26645, 26646, 26647, 26648, 26649, 26650, 26651, 26652, 26653, 26654, 26655, 26656, 26657, 26658, 26659, 26660, 26661, 26662, 26663, 26664, 26665, 26666, 26667, 26668, 26669, 26670, 26671, 26672, 26673, 26674, 26675, 26676, 26677, 26678, 26679, 26680, 26681, 26682, 26683, 26684, 26685, 26686, 26687, 26688, 26689, 26690, 26691, 26692, 26693, 26694, 26695, 26696, 26697, 26698, 26699, 26700, 26701, 26702, 26703, 26704, 26705, 26706, 26707, 26708, 26709, 26710, 26711, 26712, 26713, 26714, 26715, 26716, 26717, 26718, 26719, 26720, 26721, 26722, 26723, 26724, 26725, 26726, 26727, 26728, 26729, 26730, 26731, 26732, 26733, 26734, 26735, 26736, 26737, 26738, 26739, 26740, 26741, 26742, 26743, 26744, 26745, 26746, 26747, 26748, 26749, 26750, 26751, 26752, 26753, 26754, 26755, 26756, 26757, 26758, 26759, 26760, 26761, 26762, 26763, 26764, 26765, 26766, 26767, 26768, 26769, 26770, 26771, 26772, 26773, 26774, 26775, 26776, 26777, 26778, 26779, 26780, 26781, 26782, 26783, 26784, 26785, 26786, 26787, 26788, 26789, 26790, 26791, 26792, 26793, 26794, 26795, 26796, 26797, 26798, 26799, 26800, 26801, 26802, 26803, 26804, 26805, 26806, 26807, 26808, 26809, 26810, 26811, 26812, 26813, 26814, 26815, 26816, 26817, 26818, 26819, 26820, 26821, 26822, 26823, 26824, 26825, 26826, 26827, 26828, 26829, 26830, 26831, 26832, 26833, 26834, 26835, 26836, 26837, 26838, 26839, 26840, 26841, 26842, 26843, 26844, 26845, 26846, 26847, 26848, 26849, 26850, 26851, 26852, 26853, 26854, 26855, 26856, 26857, 26858, 26859, 26860, 26861, 26862, 26863, 26864, 26865, 26866, 26867, 26868, 26869, 26870, 26871, 26872, 26873, 26874, 26875, 26876, 26877, 26878, 26879, 26880, 26881, 26882, 26883, 26884, 26885, 26886, 26887, 26888, 26889, 26890, 26891, 26892, 26893, 26894, 26895, 26896, 26897, 26898, 26899, 26900, 26901, 26902, 26903, 26904, 26905, 26906, 26907, 26908, 26909, 26910, 26911, 26912, 26913, 26914, 26915, 26916, 26917, 26918, 26919, 26920, 26921, 26922, 26923, 26924, 26925, 26926, 26927, 26928, 26929, 26930, 26931, 26932, 26933, 26934, 26935, 26936, 26937, 26938, 26939, 26940, 26941, 26942, 26943, 26944, 26945, 26946, 26947, 26948, 26949, 26950, 26951, 26952, 26953, 26954, 26955, 26956, 26957, 26958, 26959, 26960, 26961, 26962, 26963, 26964, 26965, 26966, 26967, 26968, 26969, 26970, 26971, 26972, 26973, 26974, 26975, 26976, 26977, 26978, 26979, 26980, 26981, 26982, 26983, 26984, 26985, 26986, 26987, 26988, 26989, 26990, 26991, 26992, 26993, 26994, 26995, 26996, 26997, 26998, 26999, 27000, 27001, 27002, 

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_24460/496428675.py in <module>
     36 
     37 D2 = pd.concat(all_results)
---> 38 D2.columns = ['timestamp', 'lane_vehicle_speed', 'lane_vehicle_count', 'occupancy', \
     39               'ds_lane_vehicle_speed', 'ds_lane_vehicle_count', 'ds_occupancy']

~\miniconda3\envs\py38_nrel\lib\site-packages\pandas\core\generic.py in __setattr__(self, name, value)
   5498         try:
   5499             object.__getattribute__(self, name)
-> 5500             return object.__setattr__(self, name, value)
   5501         except AttributeError:
   5502             pass

~\miniconda3\envs\py38_nrel\lib\site-packages\pandas\_libs\properties.pyx in pandas._libs.properties.AxisProperty.__set__()

~\miniconda3\envs\py38_nrel\lib\site-packages\pandas\core\generic.py in _set_axis(self, axis, labels)
    764     def _set_axis(self, axis: int, labels: Index) -> None:
    765         labels = ensure_index(labels)
--> 766         self._mgr.set_axis(axis, labels)
    767         self._clear_item_cache()
    768 

~\miniconda3\envs\py38_nrel\lib\site-packages\pandas\core\internals\managers.py in set_axis(self, axis, new_labels)
    214     def set_axis(self, axis: int, new_labels: Index) -> None:
    215         # Caller is responsible for ensuring we have an Index object.
--> 216         self._validate_set_axis(axis, new_labels)
    217         self.axes[axis] = new_labels
    218 

~\miniconda3\envs\py38_nrel\lib\site-packages\pandas\core\internals\base.py in _validate_set_axis(self, axis, new_labels)
     55 
     56         elif new_len != old_len:
---> 57             raise ValueError(
     58                 f"Length mismatch: Expected axis has {old_len} elements, new "
     59                 f"values have {new_len} elements"

ValueError: Length mismatch: Expected axis has 10 elements, new values have 7 elements

D2 = pd.concat(all_results)
D2.columns = ['timestamp', 'lane_vehicle_speed', 'lane_vehicle_count', 'occupancy', \
              'ds_lane_vehicle_speed', 'ds_lane_vehicle_count', 'ds_occupancy', 'us_lane_vehicle_speed', 'us_lane_vehicle_count', 'us_occupancy']

D2.set_index('timestamp', inplace=True)
D2 = D2[~D2.index.duplicated(keep='first')]
D2.head()

	lane_vehicle_speed	lane_vehicle_count	occupancy	ds_lane_vehicle_speed	ds_lane_vehicle_count	ds_occupancy	us_lane_vehicle_speed	us_lane_vehicle_count	us_occupancy
timestamp
2021-10-26 20:01:27	100.0	1.0	0.0	82	3	0	74	2	0
2021-10-26 20:02:27	79.0	3.0	0.0	86	7	2	91	1	0
2021-10-26 20:03:27	88.0	9.0	2.0	88	2	0	81	6	2
2021-10-26 20:04:27	86.0	3.0	0.0	81	2	0	84	7	2
2021-10-26 20:05:27	79.0	2.0	0.0	85	4	1	84	2	0

D2.tail()

	lane_vehicle_speed	lane_vehicle_count	occupancy	ds_lane_vehicle_speed	ds_lane_vehicle_count	ds_occupancy	us_lane_vehicle_speed	us_lane_vehicle_count	us_occupancy
timestamp
2021-11-16 10:11:06	80.0	1.0	0.0	149	0	0	91	2	0
2021-11-16 10:12:06	149.0	0.0	0.0	92	1	0	80	1	0
2021-11-16 10:13:06	89.0	1.0	0.0	86	5	1	89	1	0
2021-11-16 10:14:06	81.0	4.0	1.0	86	5	1	83	1	0
2021-11-16 10:15:06	78.0	4.0	1.0	81	8	2	82	5	1

D2.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 26268 entries, 2021-10-26 20:01:27 to 2021-11-16 10:15:06
Data columns (total 9 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   lane_vehicle_speed     26267 non-null  float64
 1   lane_vehicle_count     26267 non-null  float64
 2   occupancy              26267 non-null  float64
 3   ds_lane_vehicle_speed  26268 non-null  int64  
 4   ds_lane_vehicle_count  26268 non-null  int64  
 5   ds_occupancy           26268 non-null  int64  
 6   us_lane_vehicle_speed  26268 non-null  int64  
 7   us_lane_vehicle_count  26268 non-null  int64  
 8   us_occupancy           26268 non-null  int64  
dtypes: float64(3), int64(6)
memory usage: 2.0 MB

# clean
#   speeds > 120 = np.nan
idx1 = D2['lane_vehicle_speed'] > 119
idx2 = D2['ds_lane_vehicle_speed'] > 119
idx3 = D2['us_lane_vehicle_speed'] > 119

print(idx1.sum(), idx2.sum(), idx3.sum())

2550 2535 2641

D2.loc[idx1, 'lane_vehicle_speed'] = np.nan
D2.loc[idx2, 'ds_lane_vehicle_speed'] = np.nan
D2.loc[idx3, 'us_lane_vehicle_speed'] = np.nan

D2.shape

(26268, 9)

# sort by index
D2.sort_index(inplace=True)

#D2.to_excel(Path('c:/projects/nrel-presentation/working/D2.xlsx'))

# plot
D2.hvplot.line(y='lane_vehicle_speed', use_index=True, width=1300)

Fill in Gaps

# 11/01 and 11/02
#  fill in this data with same hours from previous day
#   missing from 2021-11-01 19:36 to 2021-11-02 13:02
#   repeat data from 2021-10-31 19:36 to 2021-11-01 13:02
D2.loc['2021-10-31 19:35' : '2021-11-01 13:03']

C:\Users\David\AppData\Local\Temp/ipykernel_24460/2187780894.py:5: FutureWarning: Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is deprecated and will raise a KeyError in a future Version.
  D2.loc['2021-10-31 19:35' : '2021-11-01 13:03']

	lane_vehicle_speed	lane_vehicle_count	occupancy	ds_lane_vehicle_speed	ds_lane_vehicle_count	ds_occupancy	us_lane_vehicle_speed	us_lane_vehicle_count	us_occupancy
timestamp
2021-10-31 19:35:21	86.0	7.0	1.0	86.0	8	2	84.0	8	2
2021-10-31 19:36:21	87.0	7.0	1.0	82.0	3	1	84.0	8	2
2021-10-31 19:37:21	79.0	9.0	2.0	85.0	6	1	89.0	3	0
2021-10-31 19:38:21	82.0	6.0	1.0	80.0	9	3	79.0	8	3
2021-10-31 19:39:21	80.0	7.0	2.0	82.0	1	0	78.0	6	2
...	...	...	...	...	...	...	...	...	...
2021-11-01 12:59:14	84.0	10.0	3.0	87.0	6	1	78.0	19	7
2021-11-01 13:00:14	86.0	2.0	0.0	82.0	17	5	80.0	8	3
2021-11-01 13:01:14	86.0	5.0	1.0	79.0	13	4	80.0	4	1
2021-11-01 13:02:14	82.0	25.0	7.0	89.0	8	2	78.0	10	4
2021-11-01 13:03:14	88.0	10.0	2.0	85.0	7	2	79.0	18	7

1011 rows × 9 columns

# add a day
newdf = D2.loc['2021-10-31 19:35' : '2021-11-01 13:03'].copy()
newindex = newdf.index.values
newdf.index = pd.DatetimeIndex([dt + pd.Timedelta('1D') for dt in newindex])
newdf.head()

C:\Users\David\AppData\Local\Temp/ipykernel_24460/984216267.py:2: FutureWarning: Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is deprecated and will raise a KeyError in a future Version.
  newdf = D2.loc['2021-10-31 19:35' : '2021-11-01 13:03'].copy()

	lane_vehicle_speed	lane_vehicle_count	occupancy	ds_lane_vehicle_speed	ds_lane_vehicle_count	ds_occupancy	us_lane_vehicle_speed	us_lane_vehicle_count	us_occupancy
2021-11-01 19:35:21	86.0	7.0	1.0	86.0	8	2	84.0	8	2
2021-11-01 19:36:21	87.0	7.0	1.0	82.0	3	1	84.0	8	2
2021-11-01 19:37:21	79.0	9.0	2.0	85.0	6	1	89.0	3	0
2021-11-01 19:38:21	82.0	6.0	1.0	80.0	9	3	79.0	8	3
2021-11-01 19:39:21	80.0	7.0	2.0	82.0	1	0	78.0	6	2

# concatenate, sort_index
D2 = pd.concat([D2, newdf])
D2.sort_index(inplace=True)

# check with plot
D2.hvplot.line(y='lane_vehicle_speed', use_index=True, width=1300)

# same thing with 2021-11-05 15:46 to 20:03
# add a day
newdf = D2.loc['2021-11-04 15:45' : '2021-11-04 20:04'].copy()
newindex = newdf.index.values
newdf.index = pd.DatetimeIndex([dt + pd.Timedelta('1D') for dt in newindex])
# concatenate, sort_index
D2 = pd.concat([D2, newdf])
D2.sort_index(inplace=True)
# check with plot
D2.hvplot.line(y='lane_vehicle_speed', use_index=True, width=1300)

C:\Users\David\AppData\Local\Temp/ipykernel_24460/1880543427.py:3: FutureWarning: Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is deprecated and will raise a KeyError in a future Version.
  newdf = D2.loc['2021-11-04 15:45' : '2021-11-04 20:04'].copy()

# same thing with 2021-11-08 12:23 to 15:23
# add a day
newdf = D2.loc['2021-11-07 12:22' : '2021-11-07 15:24'].copy()
newindex = newdf.index.values
newdf.index = pd.DatetimeIndex([dt + pd.Timedelta('1D') for dt in newindex])
# concatenate, sort_index
D2 = pd.concat([D2, newdf])
D2.sort_index(inplace=True)
# check with plot
D2.hvplot.line(y='lane_vehicle_speed', use_index=True, width=1300)

C:\Users\David\AppData\Local\Temp/ipykernel_24460/2578557417.py:3: FutureWarning: Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is deprecated and will raise a KeyError in a future Version.
  newdf = D2.loc['2021-11-07 12:22' : '2021-11-07 15:24'].copy()

# same thing with 2021-11-09 00:03 to 08:12
# add a day
newdf = D2.loc['2021-11-08 00:02' : '2021-11-08 08:13'].copy()
newindex = newdf.index.values
newdf.index = pd.DatetimeIndex([dt + pd.Timedelta('1D') for dt in newindex])
# concatenate, sort_index
D2 = pd.concat([D2, newdf])
D2.sort_index(inplace=True)
# check with plot
D2.hvplot.line(y='lane_vehicle_speed', use_index=True, width=1300)

C:\Users\David\AppData\Local\Temp/ipykernel_24460/1484982418.py:3: FutureWarning: Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is deprecated and will raise a KeyError in a future Version.
  newdf = D2.loc['2021-11-08 00:02' : '2021-11-08 08:13'].copy()

# truncate after 11/15 21:00
D2 = D2.iloc[:-1]
D2.sort_index(inplace=True)

D2 = D2.truncate(after=pd.Timestamp('2021-11-15 21:00'))
D2.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 28083 entries, 2021-10-26 20:01:27 to 2021-11-15 20:59:10
Data columns (total 9 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   lane_vehicle_speed     25271 non-null  float64
 1   lane_vehicle_count     28083 non-null  float64
 2   occupancy              28083 non-null  float64
 3   ds_lane_vehicle_speed  25297 non-null  float64
 4   ds_lane_vehicle_count  28083 non-null  int64  
 5   ds_occupancy           28083 non-null  int64  
 6   us_lane_vehicle_speed  25188 non-null  float64
 7   us_lane_vehicle_count  28083 non-null  int64  
 8   us_occupancy           28083 non-null  int64  
dtypes: float64(5), int64(4)
memory usage: 2.1 MB

# just in case, remove duplicates
D2 = D2[~D2.index.duplicated(keep='first')]

D2.hvplot.line(y='lane_vehicle_speed', use_index=True, width=1300)

Resample data

! warning. resampling is just a groupby wrt time. ideally we would create a new x-minute index and do a proper interpolation using scipy, but for now we'll just go with the resampling that pandas gives us.

Data = D2.resample('10T', label='right', closed='right').mean().interpolate()

Data.head()

	lane_vehicle_speed	lane_vehicle_count	occupancy	ds_lane_vehicle_speed	ds_lane_vehicle_count	ds_occupancy	us_lane_vehicle_speed	us_lane_vehicle_count	us_occupancy
2021-10-26 20:10:00	85.125000	2.888889	0.333333	83.375000	2.555556	0.333333	81.444444	3.000000	0.555556
2021-10-26 20:20:00	81.400000	2.500000	0.200000	80.555556	2.700000	0.500000	78.444444	2.600000	0.500000
2021-10-26 20:30:00	79.375000	2.900000	0.400000	82.250000	2.800000	0.500000	79.333333	2.700000	0.600000
2021-10-26 20:40:00	84.444444	2.900000	0.500000	84.200000	2.500000	0.300000	83.888889	1.900000	0.300000
2021-10-26 20:50:00	81.555556	2.666667	0.222222	83.625000	2.222222	0.333333	81.250000	3.111111	0.777778

Data.tail()

	lane_vehicle_speed	lane_vehicle_count	occupancy	ds_lane_vehicle_speed	ds_lane_vehicle_count	ds_occupancy	us_lane_vehicle_speed	us_lane_vehicle_count	us_occupancy
2021-11-15 20:20:00	85.875000	4.000000	0.625000	84.857143	3.00	0.500000	83.125000	2.625000	0.500000
2021-11-15 20:30:00	80.444444	4.222222	0.666667	80.777778	4.00	0.777778	77.111111	3.666667	0.888889
2021-11-15 20:40:00	79.750000	3.700000	0.600000	80.700000	4.00	0.800000	78.777778	4.000000	1.100000
2021-11-15 20:50:00	82.375000	4.500000	0.750000	81.500000	4.25	0.875000	81.142857	3.125000	0.625000
2021-11-15 21:00:00	82.600000	3.900000	0.600000	81.100000	4.20	0.900000	79.900000	4.100000	0.900000

Data.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2886 entries, 2021-10-26 20:10:00 to 2021-11-15 21:00:00
Freq: 10T
Data columns (total 9 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   lane_vehicle_speed     2886 non-null   float64
 1   lane_vehicle_count     2886 non-null   float64
 2   occupancy              2886 non-null   float64
 3   ds_lane_vehicle_speed  2886 non-null   float64
 4   ds_lane_vehicle_count  2886 non-null   float64
 5   ds_occupancy           2886 non-null   float64
 6   us_lane_vehicle_speed  2886 non-null   float64
 7   us_lane_vehicle_count  2886 non-null   float64
 8   us_occupancy           2886 non-null   float64
dtypes: float64(9)
memory usage: 225.5 KB

Data.to_excel(Path('c:/projects/nrel-presentation/working/Data.xlsx'))

Data.isnull().sum()

lane_vehicle_speed       0
lane_vehicle_count       0
occupancy                0
ds_lane_vehicle_speed    0
ds_lane_vehicle_count    0
ds_occupancy             0
us_lane_vehicle_speed    0
us_lane_vehicle_count    0
us_occupancy             0
dtype: int64

Add features

from timestamp:

• hour of day
• sin/cos (hour of day)
• day of week (one-hot encoding)
• sin(day of year / 365 2 pi)
• cos(day of year / 365 2 pi)

# hour of day
hod = Data.index.hour
Data['hod'] = hod
Data['sin_hod'] = np.sin((hod+1) / 24 * 2 * np.pi)
Data['cos_hod'] = np.cos((hod+1) / 24 * 2 * np.pi)

# day of week
#  use one-hot encoding with 6 values
dow = np.array([str(i) for i in Data.index.weekday.values]).T
s = pd.Series(dow)
tmp = pd.get_dummies(s, drop_first=True)
tmp.columns = ['is_dow_{}'.format(this_col) for this_col in tmp.columns]
for col in tmp.columns:
    Data[col] = tmp[col].values

Data.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2886 entries, 2021-10-26 20:10:00 to 2021-11-15 21:00:00
Freq: 10T
Data columns (total 18 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   lane_vehicle_speed     2886 non-null   float64
 1   lane_vehicle_count     2886 non-null   float64
 2   occupancy              2886 non-null   float64
 3   ds_lane_vehicle_speed  2886 non-null   float64
 4   ds_lane_vehicle_count  2886 non-null   float64
 5   ds_occupancy           2886 non-null   float64
 6   us_lane_vehicle_speed  2886 non-null   float64
 7   us_lane_vehicle_count  2886 non-null   float64
 8   us_occupancy           2886 non-null   float64
 9   hod                    2886 non-null   int64  
 10  sin_hod                2886 non-null   float64
 11  cos_hod                2886 non-null   float64
 12  is_dow_1               2886 non-null   uint8  
 13  is_dow_2               2886 non-null   uint8  
 14  is_dow_3               2886 non-null   uint8  
 15  is_dow_4               2886 non-null   uint8  
 16  is_dow_5               2886 non-null   uint8  
 17  is_dow_6               2886 non-null   uint8  
dtypes: float64(11), int64(1), uint8(6)
memory usage: 310.0 KB

# day of year
#   use sin/cos
doy = Data.index.day_of_year
Data['sin_doy'] = np.sin((doy+1) / 365 * 2 * np.pi)
Data['cos_doy'] = np.cos((doy+1) / 365 * 2 * np.pi)

Data.tail()

	lane_vehicle_speed	lane_vehicle_count	occupancy	ds_lane_vehicle_speed	ds_lane_vehicle_count	ds_occupancy	us_lane_vehicle_speed	us_lane_vehicle_count	us_occupancy	hod	sin_hod	cos_hod	is_dow_1	is_dow_2	is_dow_3	is_dow_4	is_dow_5	is_dow_6	sin_doy	cos_doy
2021-11-15 20:20:00	85.875000	4.000000	0.625000	84.857143	3.00	0.500000	83.125000	2.625000	0.500000	20	-0.707107	0.707107	0	0	0	0	0	0	-0.699458	0.714673
2021-11-15 20:30:00	80.444444	4.222222	0.666667	80.777778	4.00	0.777778	77.111111	3.666667	0.888889	20	-0.707107	0.707107	0	0	0	0	0	0	-0.699458	0.714673
2021-11-15 20:40:00	79.750000	3.700000	0.600000	80.700000	4.00	0.800000	78.777778	4.000000	1.100000	20	-0.707107	0.707107	0	0	0	0	0	0	-0.699458	0.714673
2021-11-15 20:50:00	82.375000	4.500000	0.750000	81.500000	4.25	0.875000	81.142857	3.125000	0.625000	20	-0.707107	0.707107	0	0	0	0	0	0	-0.699458	0.714673
2021-11-15 21:00:00	82.600000	3.900000	0.600000	81.100000	4.20	0.900000	79.900000	4.100000	0.900000	21	-0.500000	0.866025	0	0	0	0	0	0	-0.699458	0.714673

Data.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2886 entries, 2021-10-26 20:10:00 to 2021-11-15 21:00:00
Freq: 10T
Data columns (total 20 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   lane_vehicle_speed     2886 non-null   float64
 1   lane_vehicle_count     2886 non-null   float64
 2   occupancy              2886 non-null   float64
 3   ds_lane_vehicle_speed  2886 non-null   float64
 4   ds_lane_vehicle_count  2886 non-null   float64
 5   ds_occupancy           2886 non-null   float64
 6   us_lane_vehicle_speed  2886 non-null   float64
 7   us_lane_vehicle_count  2886 non-null   float64
 8   us_occupancy           2886 non-null   float64
 9   hod                    2886 non-null   int64  
 10  sin_hod                2886 non-null   float64
 11  cos_hod                2886 non-null   float64
 12  is_dow_1               2886 non-null   uint8  
 13  is_dow_2               2886 non-null   uint8  
 14  is_dow_3               2886 non-null   uint8  
 15  is_dow_4               2886 non-null   uint8  
 16  is_dow_5               2886 non-null   uint8  
 17  is_dow_6               2886 non-null   uint8  
 18  sin_doy                2886 non-null   float64
 19  cos_doy                2886 non-null   float64
dtypes: float64(13), int64(1), uint8(6)
memory usage: 355.1 KB

Min/Max Scaler

scale all values between -1 and 1. this way the sin and cos are untouched and we can use the same scaler for everything.

do it again for just lane_vehicle_speed, because later we'll need to unscale the predictions.

feature_columns = [col for col in Data.columns if col != 'lane_vehicle_speed']
Data[feature_columns]

	lane_vehicle_count	occupancy	ds_lane_vehicle_speed	ds_lane_vehicle_count	ds_occupancy	us_lane_vehicle_speed	us_lane_vehicle_count	us_occupancy	hod	sin_hod	cos_hod	is_dow_1	is_dow_2	is_dow_3	is_dow_4	is_dow_5	is_dow_6	sin_doy	cos_doy
2021-10-26 20:10:00	2.888889	0.333333	83.375000	2.555556	0.333333	81.444444	3.000000	0.555556	20	-0.707107	0.707107	1	0	0	0	0	0	-0.899631	0.436651
2021-10-26 20:20:00	2.500000	0.200000	80.555556	2.700000	0.500000	78.444444	2.600000	0.500000	20	-0.707107	0.707107	1	0	0	0	0	0	-0.899631	0.436651
2021-10-26 20:30:00	2.900000	0.400000	82.250000	2.800000	0.500000	79.333333	2.700000	0.600000	20	-0.707107	0.707107	1	0	0	0	0	0	-0.899631	0.436651
2021-10-26 20:40:00	2.900000	0.500000	84.200000	2.500000	0.300000	83.888889	1.900000	0.300000	20	-0.707107	0.707107	1	0	0	0	0	0	-0.899631	0.436651
2021-10-26 20:50:00	2.666667	0.222222	83.625000	2.222222	0.333333	81.250000	3.111111	0.777778	20	-0.707107	0.707107	1	0	0	0	0	0	-0.899631	0.436651
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2021-11-15 20:20:00	4.000000	0.625000	84.857143	3.000000	0.500000	83.125000	2.625000	0.500000	20	-0.707107	0.707107	0	0	0	0	0	0	-0.699458	0.714673
2021-11-15 20:30:00	4.222222	0.666667	80.777778	4.000000	0.777778	77.111111	3.666667	0.888889	20	-0.707107	0.707107	0	0	0	0	0	0	-0.699458	0.714673
2021-11-15 20:40:00	3.700000	0.600000	80.700000	4.000000	0.800000	78.777778	4.000000	1.100000	20	-0.707107	0.707107	0	0	0	0	0	0	-0.699458	0.714673
2021-11-15 20:50:00	4.500000	0.750000	81.500000	4.250000	0.875000	81.142857	3.125000	0.625000	20	-0.707107	0.707107	0	0	0	0	0	0	-0.699458	0.714673
2021-11-15 21:00:00	3.900000	0.600000	81.100000	4.200000	0.900000	79.900000	4.100000	0.900000	21	-0.500000	0.866025	0	0	0	0	0	0	-0.699458	0.714673

2886 rows × 19 columns

!! here!!

only do the minmax scaler on the Training data. Then apply it to the test data.

scaler = MinMaxScaler(feature_range=(-1, 1))
all_data_scaled = scaler.fit_transform(Data)
print(all_data_scaled)

[[0.64700098 0.15930388 0.12600229 ... 0.         0.         0.        ]
 [0.64424779 0.24497992 0.13402062 ... 0.         0.         0.        ]
 [0.73274336 0.14457831 0.06185567 ... 0.         0.         0.        ]
 ...
 [0.59469027 0.14056225 0.07216495 ... 1.         1.         1.        ]
 [0.58053097 0.18875502 0.10309278 ... 1.         1.         1.        ]
 [0.44247788 0.29317269 0.20618557 ... 1.         1.         1.        ]]

target_scaler = MinMaxScaler()
target_scaled = target_scaler.fit_transform(Data['lane_vehicle_speed'].values.reshape(-1, 1))

print(target_scaled[:10, :])

[[0.64700098]
 [0.64424779]
 [0.73274336]
 [0.52035398]
 [0.61946903]
 [0.79292035]
 [0.59469027]
 [0.53451327]
 [0.55575221]
 [0.67964602]]

# gut check
print(target_scaled.min(), target_scaled.max())

0.0 0.9999999999999998

# save the scalers to use later
scaler_filename = Path('c:/projects/nrel-presentation/models/scaler.save')
joblib.dump(scaler, scaler_filename)
target_scaler_filename = Path('c:/projects/nrel-presentation/models/target_scaler.save')
joblib.dump(target_scaler, target_scaler_filename)

['c:\\projects\\nrel-presentation\\models\\target_scaler.save']

# put scaled data into dataframe, add column names
# we don't necessarily need timestamps for the actual modeling, but will need them later for plotting
Data_scaled = pd.DataFrame(all_data_scaled, columns=Data.columns, index=Data.index)
Data_scaled.iloc[100]

lane_vehicle_speed       0.538053
lane_vehicle_count       0.457831
occupancy                0.288660
ds_lane_vehicle_speed    0.523564
ds_lane_vehicle_count    0.562842
ds_occupancy             0.294872
hod                      0.521739
sin_hod                  0.370590
cos_hod                  0.017037
is_dow_2                 1.000000
is_dow_3                 0.000000
is_dow_4                 0.000000
is_dow_5                 0.000000
is_dow_6                 0.000000
sin_doy                  0.187181
cos_doy                  0.203657
Name: 2021-10-27 12:40:00, dtype: float64

Data_scaled.head()

	lane_vehicle_speed	lane_vehicle_count	occupancy	ds_lane_vehicle_speed	ds_lane_vehicle_count	ds_occupancy	hod	sin_hod	cos_hod	is_dow_2	is_dow_3	is_dow_4	is_dow_5	is_dow_6	sin_doy	cos_doy
timestamp
2021-10-26 20:00:00	0.647001	0.159304	0.126002	0.491900	0.184578	0.071225	0.869565	0.146447	0.853553	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2021-10-26 20:10:00	0.644248	0.244980	0.134021	0.563328	0.240437	0.076923	0.869565	0.146447	0.853553	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2021-10-26 20:20:00	0.732743	0.144578	0.061856	0.640648	0.174863	0.051282	0.869565	0.146447	0.853553	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2021-10-26 20:30:00	0.520354	0.148594	0.082474	0.548355	0.163934	0.064103	0.869565	0.146447	0.853553	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2021-10-26 20:40:00	0.619469	0.163766	0.080183	0.631811	0.142077	0.042735	0.869565	0.146447	0.853553	0.0	0.0	0.0	0.0	0.0	0.0	0.0

Export scaled data

# save to processed data
Data_scaled.to_parquet(Path('c:/projects/nrel-presentation/data/processed/Data_scaled.pq'))

all_data_scaled.shape

(652, 16)

Data.columns

Index(['lane_vehicle_speed', 'lane_vehicle_count', 'occupancy',
       'ds_lane_vehicle_speed', 'ds_lane_vehicle_count', 'ds_occupancy', 'hod',
       'sin_hod', 'cos_hod', 'is_dow_2', 'is_dow_3', 'is_dow_4', 'is_dow_5',
       'is_dow_6', 'sin_doy', 'cos_doy'],
      dtype='object')

Plot

Data_scaled.hvplot(subplots=True, height=150).cols(1)

Scratch

import math
import matplotlib.pyplot as plt

doy = np.arange(0, 365)
sindoy = np.sin(doy / 365 * 2 * np.pi)
cosdoy = np.cos(doy / 365 * 2 * np.pi)
plt.plot(doy, sindoy)
plt.plot(doy, cosdoy, color='red')

[<matplotlib.lines.Line2D at 0x1d787081eb0>]

from sklearn.preprocessing import MinMaxScaler

A = pd.DataFrame({'a': [1, 22, 25], 'b': [10, 19, 22], 'c': [101, 111, 232], 'd': [1, 2, 3]})
A

	a	b	c	d
0	1	10	101	1
1	22	19	111	2
2	25	22	232	3

scaler = MinMaxScaler()
scaler.fit_transform(A[['b', 'c', 'd']])

array([[0.        , 0.        , 0.        ],
       [0.75      , 0.07633588, 0.5       ],
       [1.        , 1.        , 1.        ]])

z = scaler.fit_transform(Data[[col for col in Data.columns if col != 'lane_vehicle_speed']])

z.shape

(2381, 12)

Data.shape

(2381, 13)

Data = pd.read_excel(Path('c:/projects/nrel-presentation/working/Data.xlsx'))

Data.head()

	timestamp	lane_vehicle_speed	lane_vehicle_count	occupancy	ds_lane_vehicle_speed	ds_lane_vehicle_count	ds_occupancy
0	2021-10-26 20:01:00	73.0	6.0	1.0	70.0	4.0	0.0
1	2021-10-26 20:02:00	74.0	2.0	1.0	66.0	3.0	1.0
2	2021-10-26 20:03:00	69.0	6.0	2.0	63.0	4.0	1.0
3	2021-10-26 20:04:00	69.0	7.0	2.0	65.0	5.0	1.0
4	2021-10-26 20:05:00	66.0	4.0	1.0	67.0	3.0	0.0

Data.set_index('timestamp', inplace=True)

Data_scaled = pd.read_parquet(Path('c:/projects/nrel-presentation/data/processed/Data_scaled.pq'))

Data_scaled

	lane_vehicle_speed	lane_vehicle_count	occupancy	ds_lane_vehicle_speed	ds_lane_vehicle_count	ds_occupancy	hod	sin_hod	cos_hod	is_dow_2	is_dow_3	is_dow_4	is_dow_5	is_dow_6	sin_doy	cos_doy
timestamp
2021-10-26 20:00:00	0.647001	0.159304	0.126002	0.491900	0.184578	0.071225	0.869565	0.146447	0.853553	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2021-10-26 20:10:00	0.644248	0.244980	0.134021	0.563328	0.240437	0.076923	0.869565	0.146447	0.853553	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2021-10-26 20:20:00	0.732743	0.144578	0.061856	0.640648	0.174863	0.051282	0.869565	0.146447	0.853553	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2021-10-26 20:30:00	0.520354	0.148594	0.082474	0.548355	0.163934	0.064103	0.869565	0.146447	0.853553	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2021-10-26 20:40:00	0.619469	0.163766	0.080183	0.631811	0.142077	0.042735	0.869565	0.146447	0.853553	0.0	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2021-10-31 07:50:00	0.529007	0.104418	0.061856	0.683358	0.076503	0.012821	0.304348	0.933013	0.250000	0.0	0.0	0.0	0.0	1.0	1.0	1.0
2021-10-31 08:00:00	0.527434	0.072289	0.020619	0.802230	0.092896	0.025641	0.347826	0.853553	0.146447	0.0	0.0	0.0	0.0	1.0	1.0	1.0
2021-10-31 08:10:00	0.594690	0.140562	0.072165	0.599902	0.136612	0.038462	0.347826	0.853553	0.146447	0.0	0.0	0.0	0.0	1.0	1.0	1.0
2021-10-31 08:20:00	0.580531	0.188755	0.103093	0.581001	0.163934	0.051282	0.347826	0.853553	0.146447	0.0	0.0	0.0	0.0	1.0	1.0	1.0
2021-10-31 08:30:00	0.442478	0.293173	0.206186	0.631811	0.251366	0.128205	0.347826	0.853553	0.146447	0.0	0.0	0.0	0.0	1.0	1.0	1.0

652 rows × 16 columns

Data_scaled.hvplot(subplots=True, height=150).cols(1)

data_scaled_plot = Data_scaled.hvplot(subplots=True, height=150, width=800).cols(1)

import hvplot

hvplot.save(data_scaled_plot, Path('c:/projects/nrel-presentation/working/Data_scaled.png'))